The R Spatial Ecosystem

“Historical” Packages

  • rgdal: interface between R and GDAL (Geospatial Data Abstraction Library) and PROJ4 libraries: raster / vector geospatial data formats and coordinate transformation.

  • sp: classes and methods for spatial data in R.

  • rgeos: interface between R and GEOS (Geometry Engine - Open Source) library: area, perimeter, distances, dissolve, buffer, overlap, union, contains…

These packages are still widely used.

Simple Features for R

  • sf Website: Simple Features for R

  • First release: October 20, 2016

  • sp, rgeos and rgdal functionnalities in one package.

  • Easier data handling, simpler objects.

  • Tidy data: compatibility with the pipe synthax and tidyverse operators.

  • Main author and maintainer: Edzer Pebesma (also sp author)


sf objects data structure:

format sf

Using sf

Data Import

library(sf)
mtq <- st_read("data/mtq/martinique.shp")
Reading layer `martinique' from data source `/data/Projets/satRday/lecture/data/mtq/martinique.shp' using driver `ESRI Shapefile'
Simple feature collection with 34 features and 23 fields
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: 690574.4 ymin: 1592426 xmax: 736126.5 ymax: 1645660
epsg (SRID):    32620
proj4string:    +proj=utm +zone=20 +datum=WGS84 +units=m +no_defs

Projection

Get current crs with epsg code and change projection with st_transform.

st_crs(mtq)
Coordinate Reference System:
  EPSG: 32620 
  proj4string: "+proj=utm +zone=20 +datum=WGS84 +units=m +no_defs"
mtq_4326 <- mtq %>% st_transform(4326)

Data Display

Default

plot(mtq)

Only geometry

plot(st_geometry(mtq))

Centroids Extraction

mtq_c <- st_centroid(mtq)
plot(st_geometry(mtq))
plot(st_geometry(mtq_c), add=TRUE, cex=1.2, col="red", pch=20)

Distance Matrix

mat <- st_distance(x=mtq_c,y=mtq_c)
mat[1:5,1:5]
Units: m
          [,1]     [,2]      [,3]      [,4]      [,5]
[1,]     0.000 35297.56  3091.501 12131.617 17136.310
[2,] 35297.557     0.00 38332.602 25518.913 18605.249
[3,]  3091.501 38332.60     0.000 15094.702 20226.198
[4,] 12131.617 25518.91 15094.702     0.000  7177.011
[5,] 17136.310 18605.25 20226.198  7177.011     0.000

Polygons Aggregation

Simple union:

mtq_u <- st_union(mtq)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2, border = "red")

Aggregation according to a grouping variable:

library(dplyr)
mtq_u2 <- mtq %>% 
  group_by(STATUT) %>% 
  summarize(P13_POP=sum(P13_POP))
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u2), add=T, lwd=2, border = "red", col=NA)

Buffer Zone

mtq_b <- st_buffer(x = mtq_u, dist = 5000)
plot(st_geometry(mtq), col="lightblue")
plot(st_geometry(mtq_u), add=T, lwd=2)
plot(st_geometry(mtq_b), add=T, lwd=2, border = "red")

Polygon Intersection

m <- rbind(c(700015,1624212), c(700015,1641586), c(719127,1641586), 
           c(719127,1624212), c(700015,1624212))
p <- st_sf(st_sfc(st_polygon(list(m))), crs = st_crs(mtq))
plot(st_geometry(mtq))
plot(p, border="red", lwd=2, add=T)

mtq_z <- st_intersection(x = mtq, y = p)
plot(st_geometry(mtq))
plot(st_geometry(mtq_z), col="red", border="green", add=T)

Voronoi Polygons

google: “st_voronoi R sf” (https://github.com/r-spatial/sf/issues/474 & https://stackoverflow.com/questions/45719790/create-voronoi-polygon-with-simple-feature-in-r) 

mtq_v <- st_voronoi(x = st_union(mtq_c))
mtq_v <- st_intersection(st_cast(mtq_v), st_union(mtq))
mtq_v <- st_join(x = st_sf(mtq_v), y = mtq_c, join=st_intersects)
mtq_v <- st_cast(mtq_v, "MULTIPOLYGON")
plot(st_geometry(mtq_v), col='lightblue')

Other Packages

CRAN task views aim to provide some guidance which packages on CRAN are relevant for tasks related to a certain topic.

CRAN Task View: Analysis of Spatial Data:

  • Classes for spatial data
  • Handling spatial data
  • Reading and writing spatial data
  • Visualisation
  • Point pattern analysis
  • Geostatistics
  • Disease mapping and areal data analysis
  • Spatial regression
  • Ecological analysis

Maps with R

Overview

Several solutions are available:

  • ggplot2 users can have a look to ggplot2 mapping features (geom_sf) that can mix nicely with ggspatial.
  • For more advanced mapping features in a ggplot2-like syntax have a look to tmap
  • cartography is based on base graphics and allow most of basic and advanced cartographic representations.
  • mapView, Leaflet and mapdeck for interactive webmaps. Full disclosure: one of the speakers is the maintainer of cartography.

Here we will focus on cartography and do small examples with ggplot2 and mapview.

cartography

Data Preparation

library(sf)
library(dplyr)
# Import geo layers
## Communes of Seine Maritime
sm <- st_read(dsn = "data/dep76/seine_maritime.geojson", stringsAsFactors = F, quiet=TRUE)
## French departements
dep <- st_read(dsn = "data/dep76/dep.geojson", stringsAsFactors = F, quiet=TRUE)

# change projection (lambert93)
sm <- st_transform(sm, 2154)
dep <- st_transform(dep, 2154)

# Import dataset  
csp <- read.csv("data/dep76/data_seine_maritime.csv")
# merge geolayer and dataset
sm <- merge(sm, csp, by="INSEE_COM", all.x=TRUE)

# Prepare some additional contextual informations
# Extract label of main cities
cities = c("Rouen","Fécamp","Le Havre","Dieppe","Le Tréport")
labels = sm %>% filter(LIBELLE %in% cities)

library(osmdata)
# Get major roads from osm
bb      <- sm %>% st_transform(4326) %>% st_bbox()
q       <- opq(bbox = bb)
qm      <- add_osm_feature (q, key = 'highway',value = 'motorway',value_exact = FALSE)
qt      <- add_osm_feature (q, key = 'highway',value = 'trunk',value_exact = FALSE)
qp      <- add_osm_feature (q, key = 'highway',value = 'primary',value_exact = FALSE)

motorway<- osmdata_sf(qm)
trunk   <- osmdata_sf(qt)
primary <- osmdata_sf(qp)

roads    <- c(primary,trunk,motorway)$osm_lines %>% st_transform(st_crs(sm))
roads.geom = st_intersection(st_geometry(roads),sm) 

# Get the shape of the main river "La seine" 
qr    <- add_osm_feature (q, key = 'waterway')
river <- osmdata_sf(qr)
river.geom <- st_geometry(river$osm_lines %>% filter(name.fr=="La Seine")) %>% st_transform(st_crs(sm))

Proportional symbols

# (Very) simple map
library(cartography)
plot(st_geometry(sm))
propSymbolsLayer(sm, var = "act")
title("Active Population")

# Custom map of active population
par(mar=c(0.2,0.2,1.4,0.2))
bb <- st_bbox(sm)
# the bbox is used to center the map on the Seine Maritime depatement
plot(st_geometry(dep), col = "ivory", border="ivory3",  bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
plot(st_geometry(sm), col="cornsilk2", border = NA, lwd = 0.5, add=T)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
propSymbolsLayer(sm, var = "act", col="darkblue", inches = 0.6, 
                 border = "white", lwd=0.7, symbols = "square",
                 legend.style = "e", legend.pos="topleft",
                 legend.title.txt = "Labor Force\n(2014)", 
                 legend.values.rnd = 0)
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
# Scale Bar
barscale(size = 10)
# North Arrow
north(pos = "topright", col = "darkblue")
# Full layout
layoutLayer(title = "Workforce in Seine-Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            col = "darkblue", coltitle = "white", tabtitle = TRUE, 
            frame = TRUE, scale = NULL, north = FALSE)

Typology Map

# To display qualitative data
# modalities
mod <- c("agr", "art", "cad", "int", "emp", "ouv")
# labels in the legedn
modlab <- c("Agriculteurs", "Artisans","Cadres", "Prof. Inter.", "Employés", "Ouvriers")
# colors
cols <- c("#e3b4a2", "#a2d5d6", "#debbd4", "#b5dab6", "#afc2e3", "#e9e2c1")

par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
typoLayer(sm, var = "cat", 
          border = "ivory", lwd = 0.5, 
          legend.values.order = mod,
          col = cols,
          add=TRUE, legend.pos = "n")
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
legendTypo(title.txt = "Dominant Socio-Professional\nCategory", 
           col = cols, 
           categ = modlab, 
           nodata = F)
barscale(size = 10)
north(pos = "topright", col = "darkblue")
layoutLayer(title = "Workforce Distribution in Seine-Maritime", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            col = "darkblue", coltitle = "white", tabtitle = TRUE,
            frame = TRUE, scale = NULL, north = FALSE)

Choropleth Map

# Compute the share of "managers" in the active population
sm$pcad <- 100 * sm$cad / sm$act

# The getBreaks() function is used to classify the variable
bks <- getBreaks(v = sm$pcad, method = "quantile", nclass = 6)

# The carto.pal() function give access to various cartographic color palettes
cols <- carto.pal("green.pal", 3,"wine.pal",3)

# Create the map
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure",
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(sm, var = "pcad", breaks = bks, 
           col = cols, border = "grey80", 
           legend.values.rnd = 1,
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers (%)", add=T)

plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
# Add a layout
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright")

We could create the same map on a cartogram based on the active population stock.

library(cartogram)
sm_c1 <- cartogram_cont(sm, "act", 3)
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(sm_c1, var = "pcad", breaks = bks, 
           col = cols, border = "grey80",            
           legend.values.rnd = 1,
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers (%)", add=T)
# functions are dedicated to legend display
labelLayer(sm_c1 %>% filter(LIBELLE%in% cities),txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
# Add a layout
layoutLayer(title = "Managers", 
            sources = "Insee, 2018", author = "Kim, Tim & Comeetie, 2018", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE, 
            frame = TRUE, scale = 10)
north(pos = "topright", south = TRUE)

Gridded Map

These maps may allow to hide or, at least, diminish the MAUP.

# Create a grid based on sm (cell area = 4 square km)
grid <- getGridLayer(x = sm, cellsize = 4000*4000, 
                     type = "regular", var = c('cad', 'act'))
# Compute the share of managers
grid$pcad <- 100 * grid$cad / grid$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

It is also possible to create hexagonal grids.

# Create a grid based on sm (cell area = 4 square km)
grid2 <- getGridLayer(x = sm, cellsize = 4000*4000, 
                     type = "hexagonal", var = c('cad', 'act'))
# Compute the share of managers
grid2$pcad <- 100 * grid2$cad / grid2$act
# Display the map as choropleth layer
par(mar=c(0.2,0.2,1.4,0.2))
plot(st_geometry(dep), col = "ivory", border="ivory3", bg="azure", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
choroLayer(grid2, var = "pcad", breaks=bks, 
           col = cols, border = "grey80", 
           lwd = 0.4, legend.pos = "topleft", 
           legend.title.txt = "Share of managers\n(en %)", add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            tabtitle = TRUE,
            frame = TRUE, scale = 10)
north(pos = "topright")

Smoothed Map

smoothLayer() uses functions from package SpatialPosition to computes Stewart’s potentials of population.
The computation of potentials could be considered as a spatial interpolation method such as inverse distance weighted interpolation (IDW) or kernel density estimator. These models aim to estimate unknown values of non-observed points from known values given by measure points. Cartographically speaking, they are often used to get a continuous surface from a set of discrete points. However, Stewart model is mainly a spatial interaction modeling approach, with a possible secondary use for spatial interpolation.

grid$cad100 <- grid$cad * 100
par(mar=c(0.2,0.2,1.4,0.2), bg="azure")
plot(st_geometry(dep), col = "ivory", border="ivory3", 
     xlim = bb[c(1,3)], ylim =  bb[c(2,4)])
smoothLayer(x = grid, var = "cad100", var2 = "act", 
            typefct = "exponential", 
            span = 4000, beta = 2, breaks = bks, col = cols,
            legend.pos = "topleft", mask = st_buffer(sm, 0),
            legend.values.rnd = 1,
            legend.title.txt = "Share of managers* (%)", 
            border = "grey90", lwd = 0.2, add=T)
plot(st_geometry(river.geom),col="azure",lwd = 3,add=TRUE)
plot(st_geometry(roads.geom),col="#666666",lwd = 1.2,add=TRUE)
# functions are dedicated to legend display
labelLayer(labels,txt="LIBELLE",halo=TRUE,col="white",bg="black",cex=1) 
layoutLayer(title = "Managers", 
            sources = "Insee, 2018, OpenStreetMap contributors", author = "Kim, Tim & Comeetie, 2018", 
            theme = "green.pal", 
            col = "darkred", coltitle = "white", 
            postitle = "center",
            frame = TRUE, scale = 10)
north(pos = "topright")
text(x = 488000, y = 6921000,adj = 0,
     font = 3, cex = 0.8,
     labels = "* Potential smoothing\n  exponential function\n  span = 4 km, beta = 2")

Cheat Sheet

The cheat sheet displays a quick overview of cartography’s main features:

cartography cheat sheet

ggplot2

Proportional symbols layer

library(ggplot2)

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = sm %>%  st_centroid(),
          
          aes(size= act), colour="#E84923CC", show.legend = 'point') +
  scale_size(name = "Active population",
             breaks = c(100,1000,10000),
             range = c(0,20)) +
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]
  ) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Active population",
       caption = "Insee, 2018\nKim & Tim, 2018")

Choropleth layer

ggplot() +
  geom_sf(data = dep, colour = "ivory3",fill = "ivory") +
  geom_sf(data = sm, aes(fill = pcad), colour = "grey80") +
  scale_fill_gradientn(name = "Share of managers (%)",
                       
                       colours = carto.pal("green.pal", 3,"wine.pal",3),
                       values=bks/max(bks))+
  coord_sf(crs = 2154, datum = NA,
           xlim = st_bbox(sm)[c(1,3)],
           ylim = st_bbox(sm)[c(2,4)]
  ) +
  theme_minimal() +
  theme(panel.background = element_rect(fill = "azure",color=NA)) +
  labs(title = "Managers",
       caption = "Insee, 2018\nKim & Tim, 2018")

mapView and Leaflet

Proportional symbols layer

These maps, as appealing as they seem to be, are not really suitable for presenting geostatistical information. For porportional symbol both librairies lack a proper way to build lengends. Nonetheless, they can be really useful for exploratory data analysis. Both librairies are quite similar with some advantage for mapview (e.g possibility to render to canvas for bigger datasets) and some other for leaflet (e.g using custom projection different from web-meractor)

library(mapview)
# convert polygon to point and compute the circle radius for porportional area
actifs = sm %>% st_centroid() %>% mutate(radius = sqrt(act)) %>% arrange(desc(act))
# build a map by providing a map tile provider and some options for the circles.
# with mapview the circle size will stay constant whatever the zoom level is.
mapview(actifs,map.types = "Stamen.TonerLite",cex="radius",legend=FALSE,col.regions="#217844",lwd=0,alpha=0.4)

With leaflet the data must be provided in long/lat(st_transform(4326) and will be converted to the coordinate reference system of the map webmercator by default. You may chose between circle with radius specied in meter (addCircle) or pixel (addCircleMarkers) if specified in pixel the radius will stay the same whatever the zoom level is, if specified in meter it will evolve with zoom level.

library(leaflet)
leaflet(actifs %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addCircles(radius = ~radius * 30, popup = ~paste0(LIBELLE," : ",act,"actifs"), fillColor="#217844",stroke=FALSE,fillOpacity=0.4)

Gridded Map

Mapview and leaflet, enable the use and styling of psf olygons and we may for example re-use the grid build previously to show the share of seine et maritime. Default choropleth can be obtain with just three argument with mapview.

mapview(grid,map.types = "Stamen.TonerLite",zcol="pcad")

Leaflet enable fine tuning of the color scale and legend.

# define a color scale function from colors and breaks
color.scale=colorBin(cols,domain=range(bks),bins=bks)

leaflet(grid  %>% st_transform(4326)) %>% addProviderTiles(providers$Stamen.TonerLite) %>%
  addPolygons( fillColor=~color.scale(pcad),stroke=FALSE,fillOpacity=0.7) %>% 
  addLegend(colors=cols,labels=round(bks[1:(length(bks)-1)]*10)/10,title="Share of Manager (%)")

Projection

Eventually, leaflet enable some move outside web-mercator the clasical projection for webmap. You may deal with tiles build in other projection system such as lamber 93 by defining a custom leaflet crs (see gis.stackexchange resolution-from-wmts-getcapacilities-scaledenomin and mathematics-behind-converting-scale-to-resolution to deal with WMTS tiles). We use such an approach here to deal with Hypsometric tints tiles (tiles colors encode elevation) and contour of chamois and ibex population area provided provided by the french national office of hunting and wild animals.

library(leaflet)
library(sf)
epsg2154 <- leafletCRS(crsClass = "L.Proj.CRS", code = "EPSG:2154",
                       proj4def = "+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs",
                       resolution = 4838095.23807/cumprod(c(1,rep(2,15)))*0.00028,
                       origin= c(-357823.2365,7230727.3772)
)

tile_ortho = "http://tiles.craig.fr/ortho/service?service=WMTS&request=GetTile&version=1.0.0&layer=ortho_2016&style=normal&tilematrixset=lambert93&format=image%2Fjpeg&tilematrix={z}&tilerow={y}&tilecol={x}"
tile_mnt = "http://tiles.craig.fr/mnt/service?service=WMTS&request=GetTile&version=1.0.0&layer=relief&style=default&tilematrixset=lambert93&format=image%2Fpng&tilematrix={z}&tilerow={y}&tilecol={x}"

tile_attrib <- "Map data &copy; <a href='https://www.craig.fr/contenu/1377-flux-tuile-wmswmtstms'> craig / IGN </a> and <a href='http://www.oncfs.gouv.fr/Cartographie-ru4/Le-portail-cartographique-de-donnees-ar291'>ONCF</a>"

bouq = read_sf("./data/BOQ_2017_massif_uni/BOQ_2017_massif_uni_L93.shp")
cha = read_sf("./data/CHA_2017_massif_uni/CHA_2017_massif_uni_L93.shp")

leaflet(options = leafletOptions(worldCopyJump = F, crs = epsg2154)) %>%
 # addTiles(urlTemplate = tile_ortho,attribution = tile_attrib) %>% 
  addTiles(urlTemplate = tile_mnt,attribution = tile_attrib) %>% 
  addPolygons(data=bouq %>% st_transform(4326),fillColor='#50162D',color='#772A7F',fillOpacity=0.05,opacity = 1,weight=2.5,dashArray = "3") %>% 
  addPolygons(data=cha %>% st_transform(4326),fillColor='#88AA00',color='#CCFF00',fillOpacity=0.05,opacity = 1,weight=2.5,dashArray = "3") %>% 
  setView(2.692174, 45.067230, 4)

reproducibility

Always share your R and packages configuration !

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
 [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
 [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] leaflet_2.0.1     bindrcpp_0.2.2    mapview_2.6.3    
 [4] ggplot2_3.0.0     cartography_2.0.2 sp_1.3-1         
 [7] dplyr_0.7.5       sf_0.6-3          rmdformats_0.3.4 
[10] knitr_1.21       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        lattice_0.20-35   png_0.1-7        
 [4] class_7.3-14      assertthat_0.2.0  digest_0.6.18    
 [7] mime_0.6          R6_2.3.0          plyr_1.8.4       
[10] stats4_3.4.3      evaluate_0.12     e1071_1.6-8      
[13] highr_0.7         pillar_1.2.1      rlang_0.3.1      
[16] lazyeval_0.2.1    rstudioapi_0.7    miniUI_0.1.1.1   
[19] raster_2.5-8      rmarkdown_1.11    labeling_0.3     
[22] webshot_0.5.0     stringr_1.3.1     questionr_0.7.0  
[25] htmlwidgets_1.3   munsell_0.4.3     shiny_1.1.0.9000 
[28] compiler_3.4.3    httpuv_1.4.4.9001 xfun_0.4         
[31] pkgconfig_2.0.1   base64enc_0.1-3   rgeos_0.3-28     
[34] htmltools_0.3.6   tidyselect_0.2.4  tibble_1.4.2     
[37] bookdown_0.9      viridisLite_0.3.0 withr_2.1.2      
[40] later_0.7.5       grid_3.4.3        jsonlite_1.6     
[43] spData_0.2.8.3    satellite_1.0.1   xtable_1.8-3     
[46] gtable_0.2.0      DBI_1.0.0         magrittr_1.5     
[49] units_0.6-0       scales_0.5.0.9000 stringi_1.2.4    
[52] promises_1.0.1    tools_3.4.3       glue_1.3.0       
[55] purrr_0.2.5       crosstalk_1.0.0   yaml_2.2.0       
[58] colorspace_1.3-2  classInt_0.2-3    bindr_0.1.1